The Making of the Machiavellian Brain:A Structural MRI Analysis

Willem J. M. I. Verbeke,Wim J. R. Rietdijk,

Wouter E. van den Berg,Roeland C. Dietvorst, and Loek Worm

Erasmus University Rotterdam

Richard P. BagozziUniversity of Michigan

Machiavellianism is a personality style marked by the use of such tactics as deceptionand manipulation so as to perform well and achieve power, status, or material wealth.Based on the assumption that Machiavellians are very sensitive to status seeking insocial contexts, which leads over time to changes in neuroplasticity, we hypothesizeand find that salespeople who score high versus low on the Mach IV Machiavellianismscale exhibit changes in gray matter volume in the following brain regions: basalganglia, prefrontal cortex, insula, and hippocampus. A Voxel-Based Morphometry(VBM) advanced MRI technique is used to test the hypothesis on a sample of 43healthy salespeople. Conjectures are made on the linkage between social psychologicalresearch on Machiavellianism and neuroscience research on the brain regions inquestion as they relate to social behavior.

Keywords: voxel-based morphometry (VBM), Machiavellian, neuroplasticity, hippocampus,prefrontal cortex

Machiavellians strive to achieve power,status, or material wealth and use a variety ofsocially offensive or unethical tactics to doso. Machiavellians show distrust toward oth-ers, regarding them as threats in their questfor status and material rewards (Dahling,Whitaker, & Levy, 2009). Research into Ma-chiavellianism has been mainly conducted bypsychologists and organization theorists andnearly exclusively is based on the results of

pen-and-paper questionnaires; prior to thecurrent study, no one has investigated theneurological correlates of Machiavellianismto our knowledge.

There is a large amount of literature aboutMachiavellians: compared to non-Machiavel-lians they are ambitious and seek external goals(e.g., status and wealth), lack emotional andsocial understanding, show little empathy, andexhibit less emotional intelligence and generalintelligence (e.g., Austin, Farrelly, Beck, &More, 2007; Barlow, Qualter, & Stylianou,2010; Ali, Sousa Amorin, & Chamorro-Premuzic, 2009; Paulhus & Williams, 2002;Jones & Paulhus, 2009). They have a short-termsocial relationship orientation (Chabrol, VanLeeuwen, Rodgers, & Sejourne, 2009), and fre-quently defect from trusted exchange relation-ships (Gunnthorsdottir, McCabe, & Smith,2002); they “strike first” without prior provoca-tion (Wilson, Near, & Miller, 1996), do notengage in much contextual performance (Dahl-ing et al., 2009), neither do they perform betterthan low Machs within different sorts of workenvironments (except for unstructured organi-zations) (Jones & Paulhus, 2009; Christie &Geis, 1970). Machiavellians fanatically display

Willem J. M. I. Verbeke, Erasmus School of Economicsand Institute for Sales and Account Management, ErasmusUniversity Rotterdam, Rotterdam, The Netherlands; WimJ. R. Rietdijk and Loek Worm, Erasmus School of Econo-mics, Erasmus University Rotterdam; Wouter E. van denBerg and Roeland C. Dietvorst, Institute for Sales andAccount Management, Erasmus University Rotterdam;Richard P. Bagozzi, Ross School of Business and College ofPharmacy, University of Michigan.

The authors would like to express their gratitude to Dr.Caroline K. L. Schraa-Tam for help in data analysis in thisresearch and to ISAM-Neuroscience at the Erasmus Schoolof Economics for sponsoring the research.

Correspondence concerning this article should be ad-dressed to Wim J. R. Rietdijk, Erasmus School of Econom-ics, Erasmus University Rotterdam, Burgemeester Oud-laan 50, 3000DR Rotterdam, The Netherlands. E-mail:rietdijk@ese.eur.nl

Journal of Neuroscience, Psychology, and Economics © 2011 American Psychological Association2011, Vol. 4, No. 4, 205–216 1937-321X/11/$12.00 DOI: 10.1037/a0025802

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“a strategy of social conduct that involves ma-nipulating others for short-term personal gain,often against the other’s self-interest” (Wilsonet al., 1996), engage in emotional manipulation(Austin et al., 2007), and in the work place seemto be able to impress or ingratiate themselveswith supervisors despite their low (mediocre)performance (Struthers, Weiner, & Allred,1998).

Given these diverse findings the questionmay be asked whether a neuroscience inves-tigation can provide a more coherent profileof Machiavellians. Inspired by Paulhus andWilliams (2002) we propose a hypotheticalprofile: High Machiavellians exhibit on aver-age relatively low mental, social, and emo-tional abilities, yet their strong aspirations forsocial status and wealth (long term orienta-tion) and their need for achievement are ac-companied by frustration (e.g., Dahling et al.,2009 show they experience low satisfaction atwork) that transforms their social environ-ments into competitive arenas. To compen-sate for this state of tension (strong desires/high frustration), they develop a cynicalworldview (an indication of disgust towardothers) and fanatically, if not compulsively,try to outsmart people with behavior (winningat any cost; Ryckman, Thornton, & Butler,1994) that, when required, takes on malevo-lent interpersonal orientation (Paulhus & Wil-liams, 2002). For example, with their“sneaky” deceptive perspective on life, theylook for situations that allow them to cheatwithout being caught, such that their status isnot threatened overtly (Shultz, 1993), or inorder to hide their mediocre performance,they seek to ingratiate themselves with man-agers by using bluff or charm (Struthers et al.,1998). In what follows, we label these so-cially undesirable social strategies as compul-sive (fanatic) engagement in sneaky socialand political strategies—so as to achieve theirlong-term goals, which is status and wealth.

Machiavellian compulsive engagement insneaky social and political maneuvering is be-lieved to begin at a young age (McIlwain, 2003;Repacholi, Slaughter, Pritchard, & Gibbs, 2003),and this way of interacting with the social envi-ronment affects how the Machiavellian brain de-velops following principles of neuroplasticity.

Neuroplasticity

Neuroplasticity reflects changes in the synap-tic organization of the brain due to experiencesgained in the social environment; such changesthen affect subsequent interaction with the en-vironment. Although neuroplasticity is more in-tense at a young age, it continues throughoutlife as, for example, is demonstrated by a per-son’s capacity to recover from injury (Buono-mano & Merzenich, 1998).

Several cases of neuroplasticity deserve men-tion. Humans change physically in response toenvironmental stimulation and recover from in-jury (Johnston, 2009). Clinical abnormalities inthe thickness and folding of the cerebral cortexhave been observed in patients with congenitaland developmental disorders such as schizo-phrenia (Bangalore et al., 2008; Kubicki et al.,2002; Leung et al., 2009), autism (Abell et al.,1999; Boddaert et al., 2004), bipolar disorders(Lyoo et al., 2004), Down’s syndrome (White,Alkire, & Haier, 2003), Parkinson’s disease(Price et al., 2004), Alzheimer’s disease (Karaset al., 2004), and Huntington’s disease (Thiebenet al., 2002). It has also been shown that con-tinuous interaction with the environment resultsin local changes in brain structure and producesmultiple, dissociable changes in the brain in-cluding increases in dendritic length, increasesor decreases in spine density, synapse forma-tion, increased glial activity, and altered meta-bolic activity (Kolb & Whishaw, 1998). Thiscortical plasticity can be reflected at a structurallevel; for example, brain structures differ be-tween musicians and nonmusicians (Gaser &Schlaug, 2003), jugglers and nonjugglers (Dra-ganski et al., 2004), students who either have orhave not studied for an examination (Draganskiet al., 2006), and between the “navigation”brain of taxi drivers and non-taxi drivers(Maguire et al., 1998).

These findings suggest that changes in graymatter volume can be induced over time by wayof continuously interacting with the social en-vironment. We now make the following hypoth-eses based upon on earlier (hypothetical) behav-ioral profiling of the Machiavellian. Given theirstrong need for status and material rewards,Machiavellians experience frustration and stresson encountering a social environment that chal-lenges their quest. High Machiavellians cope bysuppressing negative feelings of frustration and

206 VERBEKE ET AL.

dissatisfaction at work by framing their aver-sion to the threatening social environment incynical world views. Their ever-growing desire(ambition) activates the dopamine system intheir brain, which rewards the development ofsneaky social and political strategies to achievetheir own social goals (status and wealth) and,thus, not necessarily those of a community orgroup to which they belong (impulsive regula-tion strategy Jones & Paulhus, 2009; p. 94).Therefore, we surmise that the following brainareas should exhibit differences in size whenhigh versus low Machiavellians are compared:areas related to reward seeking, especially in asocial context (basal ganglia); areas involved insocial planning (e.g., prefrontal cortex); areasrelated to coping with or blunting of negativeemotions and frustrations (e.g., insula and pre-frontal cortex); and finally, areas related tomemory consolidation, retrieval, and learning(hippocampus), which reflect constant learningto develop political and social strategies in spe-cific social contexts. In what follows, we elab-orate on these conjectures.

The basal ganglia consist of the striatum andpallidum. The striatum consists of two maincomponents: the caudate and putamen. The cau-date is an important part of the learning andmemory system. It is highly innervated by theneurons of the ventral tegmental area (VTA).The VTA is part of the reward system and isinvolved in motivated behavior (goal seeking).The putamen plays a role in social cognition,especially in intuitive social actions (Lieber-man, 2000) and in predictions of other people’sactions (Delgado, Frank, & Phelps, 2005). Thebasal ganglia are well positioned to respond toincentive reward motivation and to pregoal at-tainment of positive affect arising from progres-sion toward a desired goal (Davidson & Irwin,1999). The pallidum consists of two parts, alarge structure called the globus pallidum andthe ventral pallidum. The globus pallidum ispart of the corticobasal ganglia loop functionand can be divided in two functionally distinctparts: the internal and external segments.

The prefrontal cortex and, more specifically,the orbital frontal cortex (OFC) is the center ofthe brain involved in planned social behavior(Lieberman, 2007), self-monitoring, and appro-priate interpersonal behavior (Beer, John, Sca-bini, & Knight, 2006). The OFC is responsiblefor predicting improvement after the experience

of punishing outcomes (Wrase et al., 2007).O’Doherty, Critchley, Deichmann, and Dolan(2003) demonstrated that the lateral OFC re-sponds to reversals in reward contingencies andpredicts changes in (social) behavior that are asstrategic as those characteristic for Machiavel-lians. The OFC has a fundamental role inmaking behavioral choices, particularly in in-completely specified or unpredictable situations(Elliott, Dolan, & Frith, 2000). At the neurallevel, the OFC is crucially involved in the mo-tivational control of goal-directed behavior(Tremblay & Schultz, 1999). The OFC is alsoassociated with voluntary suppression of nega-tive emotion, top-down regulation of autonomicor peripheral physiological responses of emo-tional experiences (Ohira et al., 2006; Phillips,Drevets, Rauch, & Lane, 2003), and in reversallearning (Fellows & Farah, 2003; Hornak et al.,2004; Kringelbach & Rolls, 2004; Rolls, 2000).

The insula has bidirectional connections toboth the amygdala and nucleus accumbens andthe orbital frontal cortex. The insula cortex isespecially involved in suppressing of emotionalinformation (coping) (Ochsner, Bunge, Gross,& Gabrielli, 2002). Furthermore, the insula cor-tex is involved in the encoding of (subjective)feelings of inequity, in making social risky de-cisions, and risk perception (Kuhnen & Knut-son, 2005; Preuschoff, Quartz, & Bossaerts,2008).

The hippocampus is important for formationand retrieval of memories about declarative andcontextual information and experienced events(e.g., episodic or autobiographical memory).This distinction is important as psychopathsshow lower hippocampus involvement (Laaksoet al., 2001), whereas Machiavellians relatecontextual information to rewards.

We investigate whether salespeople exhibitdifferences in gray matter morphometry usingvoxel-based morphometry (VBM), taking intoaccount their score on a Machiavellian scale andcontrolling for age and years of sales experi-ence. We use age and years of experience asconfounding factors because they are the twolargest determinants of development of grayand white matter (Walhovd et al., 2005). Theuse of advanced MRI analysis, such as VBM,the psychological Machiavellian scale, and theempirical setting of sales employed in this studymake an original contribution to the literature.

207MRI ANALYSIS OF THE MACHIAVELLIAN BRAIN

Because salespeople interact with multiplestakeholders from both inside (colleagues) andoutside (customers) their organization, it is dif-ficult for managers to closely observe and con-trol their social behavior, which affords oppor-tunities to Machiavellians to exert their sneakysocial and political strategies (e.g., Verbeke,Ouwerkerk, & Peelen, 1996). Machiavelliansare capable of influencing social peers (e.g.,colleagues) and confederates and outmaneuver-ing them to forestall revenge (e.g., Dahling etal., 2009; McIlwain, 2003; Wilson et al., 1996).Moreover, they are known to display charm inforging calculated short-term alliances to createinternal organizational support (Wilson et al.,1996). In this study, we investigate whethercontinued engagement in Machiavellian behav-ior, including such instances as seeking oppor-tunities that allow them to reach their own long-term goals (status and wealth), defecting fromrelationships when needed, creating short-termalliances, and ingratiating others to hide lowperformance and intelligence to reach their longterm goals, affects the brain development ofMachiavellian salespeople.

Method

In a study of 43 healthy participants (sales pro-fessionals), we compared participants’ scores on astandard psychological test for measuring Machi-avellianism with the results of their MRI scans.We hypothesize that people with high scores onMachiavellianism will show structural differencesin brain size as compared to individuals with lowscores on Machiavellianism.

Machiavellianism Test

Participants completed the Mach-IV test, thestandard written psychological test for measur-ing Machiavellianism (Christie & Geis, 1970).The test measures a person’s tendency to ma-nipulate others to achieve personal goals,among related characteristics. It uses 20 state-ments rated on 7-point Likert scales rangingfrom 1 � totally agree to 7 � totally disagree.The psychometric properties of the test haveproven satisfactory in earlier research as well asin this study (e.g., Cronbach’s alpha exceededthe critical threshold of .70; Nunnally & Bern-stein, 1994). In the literature (Christie & Geis,1970), an average score of 4 on the 7-point scale

is widely used for dividing the participants intolow and high Machiavellians; above 4 meanshigh Machiavellian, all other scores are low.High Machiavellians endorse statements suchas “Never tell anyone the real reason you didsomething unless it is useful to do so” (state-ment no. 1), but not ones like “Most people arebasically good and kind” (no. 4). Low Machia-vellians tend to believe “There is no excuse forlying to someone else” (no. 7) and “Most peoplewho get ahead in the world lead clean, morallives” (no. 11).

Structural Scan

The Medisch Etische Toetsingscommissie(Medical Ethics Committee) of the local aca-demic medical center approved the researchproject. Participants were informed both ver-bally and in writing about the research project.Contraindications for MRI examination werechecked for in advance. All 43 participants werescanned by a 3-D MRI machine using a dedi-cated eight-channel head coil. To obtain theanatomical image we used a 3-D high-resolu-tion inversion recovery fast spoiled gradientrecalled echo sequence (echo time (TE)/repetition time (TR)/inversion time � 2.1/10.4/300 ms, flip angle � 18°, matrix � 416 � 256,field of view (FOV) � 25 cm, slice thick-ness 1.6 mm with 50% overlap).

Statistical Analysis

Brain structure was analyzed using VBM,which generally includes the following steps: 1)divide the participants into two relevant groupsfor comparison on the basis of their Machiavel-lian scores, 2) brain extraction, meaning thatone excludes the skull from each subject’s data,3) normalization with modulation: align allbrains at the voxel level using nonrigid regis-tration, 4) segmentation: segment the gray mat-ter of individual brains, 5) smoothing: smooththe gray matter image with a full width at halfmaximum (FWHM) of 12 mm in all three di-rections (and segmentation quality checks), 6)analyze the group level differences, and 7) pro-duce an image as a significance map to identifythe detected differences. The software package,Statistical Parametric Mapping 5 (SPM5), wasimplemented in the VBM5 toolbox, which iswidely used for analyzing both functional MRI

208 VERBEKE ET AL.

and other structural brain-imaging data se-quences. For the statistical analysis, VBM5 usesthe general linear model (GLM) to identify re-gions of gray matter that are significantly re-lated to the particular effects under study. GLMis a flexible framework that allows many differ-ent tests to be applied, ranging from groupcomparisons and identifying differences in theregions of gray or white matter structures interms of “concentration” or “volume” related tospecified covariates such as disease severity orage, to complex interactions between variouseffects of interest.

For this study, we performed two consecutiveGLM tests to identify the absolute amount ofgray or white matter in various regions andcompare these between low and high Machia-vellians. The first GLM is the segmented brainswith the variable high/low Machiavellianismscore controlled for the covariate age. The sec-ond GLM incorporated, besides age, the num-ber of years of sales experience as a controlvariable. Equation (1) presents the second GLMmodel employed using both age and years ofsales experience as covariates.

�n � �0 � �1X1 � �2X2 � �3X3 � εn (1)

Where: �n � Structural MRI data; X1 �Machiavellian test score; X2 � Age; X3 �Years of Sales Experience.

Results

Personal data were recorded for all partici-pants (N � 43, mean age � 34.67, SD � 7.29;Table 1), and the absolute and relative scoresfor the Mach-IV test were calculated for eachparticipant. The relative score of the Mach-IVtest was calculated as follows: participants witha score higher than 4 were rated high, all otherparticipants were low. Following this calcula-tion, 24 participants were classified as low Ma-chiavellians and 19 as high Machiavellians. Therelative Mach-IV test scores were used for fur-ther analysis. The number of years of experi-ence was self-reported by the subject (averageyears of experience � 8.57, SD � 5.77).

The structural scan for each participant wassegmented using the VBM5 toolbox in SPM5.The estimating options used for normalizingand segmenting were tissue probability maps,

Gaussian per class 2 2 2 4, affine regulariza-tion with the European ICBM space template,warping regularization 1, warping frequency cut-off 25, very light regularization, FWHM 70 mmcut-off, sampling distance 3, and with the center ofmass option set to origin. The writing option in-cluded modulated/normalized options for all whiteand gray matters and for the cerebrospinal fluid.The extended option included: the option off tis-sue priors; the iterative hidden Markov randomfield (HMRF) weighting; the light clean-up parti-tions; voxel size for normalization 1 1 1; boundingbox �78 �112 �70, 78 76 85; and the warps andaffine for writing data.

After segmentation, the gray matter imageswere smoothed using a Gaussian-smoothingkernel with the FWHM set to 12 mm in all threedimensions to suppress noise and effects due toresidual differences in functional and gyralanatomy during interparticipant averaging. Ascommon practice in an VBM analysis, the re-sults were then checked for outliers and insuf-ficient (bad quality) segmentation (Bookstein,2001; Bullmore, Suckling, Rabe-Hesketh, Tay-lor, & Brammer, 1999). If a study has a reason-able sample size, artifacts are easily overseen.In order to identify images with poor imagequality or even artifacts, one can use the func-tion “Check sample homogeneity using stan-dard deviation across sample” (i.e., Figure 1Homogeneity Plot). To use this function, im-ages have to be in the same orientation withsame voxel-size and dimension (e.g., normal-ized images). The idea of this tool is to checkthe standard deviation across the sample. Stan-dard deviation is calculated by the sum of thesquared distance of each image from the samplemean. Hence, the squared distance of one imagefrom the sample means represents the amount towhich this images deviates from the samplemean. A large distance to mean does not alwaysindicate that this image is an outlier or containsan artifact. Usually images of (sick) patients aremore deviant from the sample mean and this iswhat one tries to localize; meaning that oneshould include them in the sample. If there areno artifacts in the image and if the image qualityis reasonable (close to the sample mean), onedoes not have to exclude this image from thesample. In our study, we have healthy subjects,meaning that large deviations shown in the plotmean poor quality structural T1-data or artifactson the scans, meaning that including these in the

209MRI ANALYSIS OF THE MACHIAVELLIAN BRAIN

results would bias the outcome of the study. Abox plot (see Figure 1) was generated to give anindication of the quality of the samples andpermitted further inspection to identify the qual-ity of the participant’s segmentation process.This resulted in the exclusion of six participants

(0119, 0204, 0207, 0212, 0213, and 0305), thusleaving 37 participants for further analysis. An-other check for normal distribution was per-formed on the remaining 37 participants usingthe one-sample Kolmogorov–Smirnov test (forsimilar procedures, see Bookstein, 2001; Bull-

Table 1Sample Description

Subject Gender Age Experience

Mach score

Absolute Relative

101 M 45 15 4.86 High102 M 33 6 6.14 High103 M 32 8 5.71 High104 M 32 4 5 High108 F 42 4 4.57 High109 M 35 7 3.71 Low110 M 29 2 4.14 High111 F 37 0 4.86 High112 M 40 15 5.57 High113 M 57 20 5.57 High114 M 43 10 4.43 High115 M 18 1 4.14 High116 M 42 4 4.43 High117 M 35 11 5.43 High119 M 34 3 5.43 High120 F 27 6 3.29 Low122 M 26 7 4.71 High201 F 34 5 1.43 Low202 M 30 12 5.14 High203 M 42 8 2.57 Low204 F 38 9 4.43 High205 M 28 3 3.71 Low206 M 44 2 2.14 Low207 M 29 2 1.86 Low208 M 30 10 2.71 Low209 M 30 3 3 Low210 F 36 8 3.29 Low211 M 34 17 2.71 Low212 M 27 15 3.57 Low213 M 38 12 4.71 High215 M 33 6 2.86 Low216 F 36 5 2.43 Low217 M 30 10 4 Low218 M 28 8 3 Low220 M 46 10 3.14 Low221 M 42 3 2.71 Low222 M 28 6 4.71 High301 F 21 6 2.86 Low302 F 28 3 3.86 Low303 F 42 22 1.57 Low304 M 36 10 3.14 Low305 F 29 4 3 Low306 M 35 17 3.71 Low

Note. The table depicts the descriptive statistics of participants in terms of gender, age, yearsof experience in sales, and the absolute and relative values scored on the Machiavellian scale.

210 VERBEKE ET AL.

Figure 1. Sample homogeneity box plot from the VBM5 toolbox. VBM5 toolbox foranalyzing sample homogeneity in a box plot, as common practice in VBM analysis. A boxplot was generated to give an indication of the quality of the samples and permitted furtherinspection to identify the quality of the participant’s segmentation process (Bookstein, 2001;Bullmore et al., 1999). This resulted in the exclusion of six healthy participants (0119, 0204,0207, 0212, 0213, and 0305), thus leaving 37 healthy participants for further analysis.

211MRI ANALYSIS OF THE MACHIAVELLIAN BRAIN

more et al., 1999). The Mach-IV scores re-mained normally distributed, with 21 partici-pants now classified as low Machiavelliansand 16 as high Machiavellians.

The statistical model employed was the fac-torial design two-sample t test with age andexperience as covariates. Listed below are themodel with no covariates, only age or onlyexperience as single variable and both age andexperience as covariates. By using age and ex-perience as confounding variables, it is possibleto remove variance that is not explained bygroup differences (Gaser, 2010).

The first GLM model with only age as acovariate showed 14 positive differences inbrain size between high and low Machiavel-lians. Table 2 and Figure 2 show the findings.Specifically, the following differences werefound in brain size between high versus lowmachavellians: caudate (R/L), pallidum (R/L),putamen (R/L) (all of which are involved inreward seeking or motivation); the insula (R/L)(involved in suppression of negative emotionsand the experience of disgust); the inferior oper-cular frontal gyrus (R), orbital frontal gyrus(R/L), inferior triangular frontal gyrus (L), su-perior medial frontal gyrus (R/L), middle fron-tal gyrus (R/L), superior frontal gyrus (L), su-perior temporal gyrus (L), and the calcarine (R)(involved in cognition, specifically social strat-

egizing); and the hippocampus (R) and parahip-pocampal gyrus (L) (involved in learning aboutsocial contexts allowing them to outsmart oth-ers). However, we found no significant negativedifferences in brain size with Machiavellianism.When both age and number of years of experi-ence were used as covariates in a second GLMmodel, the results were the same as shown inTable 2 and Figure 2, except that the calcarinerevealed no differences between high and lowMachiavellians.

Including the six participants into the sam-ple and the two covariates results in two moreareas that show significantly positively differ-ences from low versus high Machiavellians.However, due to bad quality of these images,these results are biased and will not be takeninto further consideration. The two additionalareas found were, (1) the amygdala (R;x � 26, y � 0, z � �22; t-value � 2.21;cluster size � 76); an area important in neg-ative emotion processing, and (2) the precen-tral gyrus (L; x � �4, y � �24, z � 62;t-value � 2.21; cluster size � 41); an areaimportant in the mirror neuron system.

Discussion

In this VBM-based study, our hypotheseswere built around the general conjecture that

Table 2Positive Significant Differences in High Versus Low Machiavellians

Cluster size T-value Anatomical areas X Y ZBrodmann

area Hemisphere Functionality

174 5.27 Caudate �14 40 �12 17 R Learning/Memory16 5.27 Hippocampus 22 6 �20 21 R Memory/

320 5.17 Insula �40 6 �16 13 L Emotions/Disgust427 5.27 Pallidum �36 �88 �4 6 R Motivation192 5.17 Pallidum �36 �88 �4 6 L Motivation

1417 5.27 Putamen �36 �88 �4 21 R Learning1678 5.17 Putamen �36 �88 �4 21 L Learning

24 4.19 Inferior Orbital Frontal Gyrus �48 58 �12 12 L Cognition89 4.19 Inferior Triangular Frontal Gyrus �53 20 12 45 L Cognition

426 5.53 Middle Frontal Gyrus 56 �2 50 10 L Cognition501 4.19 Middle Orbital Frontal Gyrus 10 58 �12 12 L Cognition481 5.53 Superioir Frontal Gyrus �12 60 34 11 L Cognition

5 5.53 Superior Medial Frontal Gyrus �4 4 68 10 L Cognition8 4.05 Calcarine �36 �88 �4 17 R Visual

Note. Positive significant differences in high versus low Machiavellians: Areas of larger brain size in the model with onlynumber of years of sales experience as covariate showing cluster size, T-values of the local maximum, the anatomical areas,the associated Brodmann areas, and the gross functionality. All areas were thresholded at p � .05 with FDR correction formultiple comparisons and with a minimum cluster size of five voxels. (L � left hemisphere, R � right hemisphere).

212 VERBEKE ET AL.

Machiavellians strongly seek status and wealth(long-term goals), despite their average, if notrelatively low, social, emotional, and intellec-tual abilities. They develop social strategies (re-warded by the dopamine system) to circumventsetbacks, hide their mediocre performance, orachieve goals quite frequently at the expense ofothers, and because they perceive colleagues ascompetitors or threats to their status-seekingambitions, they feel entitled to violate socialnorms with impunity (i.e., they have cynicalworld views). In short they compulsively (fa-natically) engage in sneaky political maneuver-ing within their social environment.

Significant positive differences for high ver-sus low Machiavellianism were found in thebasal ganglia (a reward center), left prefrontalcortex (used in planning to outsmart people andregulation of negative feelings), bilaterally inthe insula (implicated in the experience ofdisgust and the need to suppress negativeemotions/frustrations), and in the right hip-pocampus and the left parahippocampal gyrus

(involved in learning and contextual informa-tion processing). All these areas are related toMachiavellian tendencies such as engaging insneaky political maneuvering (e.g., doingwhatever it takes to get what they want with-out getting caught such that their social statusis not endangered).

There are, of course, limitations to our re-search. Indeed, our GLM model controlled foronly two sociodemographic variables; namely,experience and age, whereas other control vari-ables could be investigated, such as mentalabilities, political skills, and/or emotional intel-ligence, all of which are now part of the hy-potheses. However, to study such control vari-ables requires larger sample sizes. In addition,the use of questionnaires may not be the mostproductive way to study human behavior; bio-markers (e.g., hormones) may be more reliable,as exemplified in the fields of socioneurosci-ence and neuroeconomics.

Our study could be complemented by geneticanalysis, focusing, for example, on genes asso-

Figure 2. Anatomical Areas. The anatomical areas found in the statistical model that usedboth age and years of sales experience as covariates. Anatomical areas with significantdifferences in size between high and low Machiavellians: A) parahippocampal gyrus, B)hippocampus, C) insula, D) caudate, E) pallidum, F) putamen, G) superior medial frontalgyrus, H) calcarine, I) rolandic opercular gyrus, J) inferior opercular frontal gyrus, K) superiortemporal gyrus, L) inferior triangular frontal gyrus, M) superior orbital frontal gyrus, N)middle orbital frontal gyrus, O) inferior orbital frontal gyrus, P) middle frontal gyrus, and Q)superior frontal gyrus.

213MRI ANALYSIS OF THE MACHIAVELLIAN BRAIN

ciated with impulsivity and addiction to exter-nal rewards such as DRD2 (e.g., Ebstein, Israel,Hong Chew, Zhong, & Knafo, 2010). Also wor-thy of further study is the question of whetherstatus seeking in high Machiavellians is associ-ated not only with a specific gene but also withbehavior learned in incidents during youth, ininteracting with parents, or from interactionsand experience on the job (epigenetics).

Our study of the Machiavellian brain struc-ture, so to speak, allows us to better understandwhat leaders could do when they take charge ofan organization (e.g., Deluga, 2001). It wouldbe interesting to compare the brains of top man-agers, especially top managers caught engagingin amoral behavior, to see whether unethicaland illegal behavior is fostered by Machiavel-lianism. Finally, a possible social benefit of ourfindings is the following: The more attentivepeople become to compulsive, sneaky Machia-vellian behavior in others, and the more theyrespond to it properly in terms of implementingsocial norms and inhibiting controls, the moreexploitive power of Machiavellians will be lim-ited and the less harm to individuals and insti-tutions will be experienced.

References

Abell, F., Krams, M., Ashburner, J., Passingham, R.,Friston, K., Frackowiak, R., . . . Frith, U. (1999).The neuroanatomy of autism: A voxel-basedwhole brain analysis of structural scans. Neurore-port, 10, 1647–1651. doi:10.1097/00001756-199906030-00005

Ali, F., Sousa Amorin, I., & Chamorro-Premuzic, T.(2009). Empathy deficits and trait emotional intel-ligence in psychopathy and Machiavellianism.Personality and Individual Differences, 47, 758–762. doi:10.1016/j.paid.2009.06.016

Austin, E. J., Farrelly, D., Beck, C., & More, H.(2007). Emotional intelligence, Machiavellian andemotional manipulation: Does EI have a darkside?. Personality and Individual Differences, 43,179–189. doi:10.1016/j.paid.2006.11.019

Bangalore, S. S., Prasad, K. M., Montrose, D. M.,Goradia, D. D., Diwadkar, V. A., & Keshavan,M. S. (2008). Cannabis use and brain structuralalterations in first episode schizophrenia—a regionof interest, voxel-based morphometric study.Schizophrenia Research, 99, 1–6.

Barlow, S. A., Qualter, P., & Stylianou, M. (2010).Relationship between Machiavellianism, emo-tional intelligence and theory of mind in children.

Personality and Individual Differences, 48, 78–82. doi:10.1016/j.paid.2009.08.021

Beer, J. S., John, O. P., Scabini, D., & Knight, R. T.(2006). Orbitofrontal cortex and social behavior: In-tegrating self-monitoring and emotion-cognition in-teractions. Journal of Cognitive Neuroscience, 18,871–879. doi:10.1162/jocn.2006.18.6.871

Boddaert, N., Chabane, N., Gervais, H., Good, C. D.,Bourgeois, M., Plumet, M. H., . . . Samson, Y.(2004). Superior temporal sulcus anatomical ab-normalities in childhood autism: A voxel-basedmorphometry MRI study. NeuroImage, 23, 364–369. doi:10.1016/j.neuroimage.2004.06.016

Bookstein, F. L. (2001). Voxel-based morphometryshould not be used with imperfectly registeredimages. NeuroImage 14, 1254–1462.

Bullmore, E. T., Suckling, O., Rabe-Hesketh, S.,Taylor, E., & Brammer, M. J. (1999). Global,voxel and cluster tests, by theory and permutationfor a difference between two groups of structuralMR images of the brain. IEEE Trans MedImag, 18, 32–42. doi:10.1109/42.750253

Buonomano, D. V., & Merzenich, M. M. (1998).Cortical plasticity: From synapses to maps. AnnualReview of Neurosience, 21, 149–186. doi:10.1146/annurev.neuro.21.1.149

Chabrol, H., Van Leeuwen, N., Rodgers, R., & Se-journe, S. (2009). Contributions of psychopathic,narcissistic, Machiavellian, and sadistic personal-ity traits to juvenile delinquency. Personality andIndividual Differences, 47, 734–739. doi:10.1016/j.paid.2009.06.020

Christie, R., & Geis, F. (1970). Studies in Machia-vellianism. New York: Academic Press.

Dahling, J. J., Whitaker, B. G., & Levy, P. E. (2009).The development and validation of a new Machi-avellianism scale. Journal of Management, 35,219–257. doi:10.1177/0149206308318618

Davidson, R. J., & Irwin, W. (1999). The functionalneuroanatomy of emotion and affective style.Trends Cognitive Science, 3, 11–21. doi:10.1016/S1364-6613(98)01265-0

Delgado, M. R., Frank, R. H., & Phelps, E. A. (2005).Perceptions of moral character modulate the neuralsystems of reward during the trust game. NatureNeuroscience, 8, 1611–1618. doi:10.1038/nn1575

Deluga, R. J. (2001). American presidential Machi-avellianism: Implications for charismatic leader-ship and rated performance. The Leadership Quar-terly, 12, 339–363. doi:10.1016/S1048-9843(01)00082-0

Draganski, B., Gaser, C., Busch, V., Schuierer, G.,Bogdahn, U., & May, A. (2004). Neuroplasticity:Changes in gray matter induced by training. Na-ture, 427, 311–312. doi:10.1038/427311a

Draganski, B., Gaser, C., Kempermann, G., Kuhn,H. G., Winkler, J., Buchel, C., & May, A. (2006).Temporal and spatial dynamics of brain structure

214 VERBEKE ET AL.

changes during extensive learning. Journal of Neu-roscience,26,6314–6317.doi:10.1523/JNEUROSCI.4628-05.2006

Ebstein, R. P., Israel, S., Hong Chew, S., Zhong, S.,& Knafo, A. (2010). Genetics of human socialbehaviors. Neuron Review, 65, 831– 844. doi:10.1016/j.neuron.2010.02.020

Elliott, R., Dolan, R. J., & Frith, C. D. (2000). Dis-sociable functions in the medial and lateral orbito-frontal cortex: Evidence from human neuroimag-ing studies. Cerebral Cortex, 10, 308–317. doi:10.1093/cercor/10.3.308

Fellows, L. K., & Farah, M. J. (2003). Ventromedialfrontal cortex mediates affective shifting in hu-mans: Evidence from a reversal learning paradigm.Brain, 126, 1830 –1837. doi:10.1093/brain/awg180

Gaser, C., & Schlaug, G. (2003). Brain structuresdiffer between musicians and non-musicians.Journal of Neuroscience, 23, 9240–9245.

Gunnthorsdottir, A., McCabe, K., & Smith, V.(2002). Using the Machiavellian instrument to pre-dict trustworthiness in a bargaining game. Journalof Economic Psychology, 23, 49–66. doi:10.1016/S0167-4870(01)00067-8

Hornak, J., O’Doherty, J., Bramham, J., Rolls,E. T., Morris, R. G., Bullock, P. R., & Polkey,C. E. (2004). Reward-related reversal learningafter surgical excisions in orbito-frontal or dor-solateral prefrontal cortex in humans. Journal ofCognitive Neuroscience, 16, 463– 478. doi:10.1162/089892904322926791

Johnston, M. V. (2009). Plasticity in the developingbrain: Implications for rehabilitation. Developmen-tal Disabilities Research Reviews, 15, 94–101.doi:10.1002/ddrr.64

Jones, D. N., & Paulhus, D. L. (2009). Machiavel-lianism. In M. R. Leary, R. H., Hoyle (Eds.),Handbook of individual differences in social be-havior (pp. 93–108). New York: Guilford Press.

Karas, G. B., Scheltens, P., Rombouts, S. A., Visser,P. J., van Schijndel, R. A., Fox, N. C., & Barkhof, F.(2004). Global and local gray matter loss in mildcognitive impairment and Alzheimer’s disease. Neu-roImage 23, 708–716. doi:10.1016/j.neuroimage.2004.07.006

Kolb, B., & Whishaw, I. Q. (1998). Brain plasticityand behavior. Annual Review of Psychology, 49,43–64. doi:10.1146/annurev.psych.49.1.43

Kringelbach, M. L., & Rolls, E. T. (2004). The func-tional neuroanatomy of the human orbitofrontalcortex: Evidence from neuroimaging and neuro-psychology. Progress in Neurobiology, 72, 341–372. doi:10.1016/j.pneurobio.2004.03.006

Kubicki, M., Shenton, M. E., Salisbury, D. F., Hirayasu,Y., Kasai, K., Kikinis, R., . . . McCarley, R. W.(2002). Voxel-based morphometric analysis of gray

matter in first episode schizophrenia. NeuroIm-age, 17, 1711–1719. doi:10.1006/nimg.2002.1296

Kuhnen, C. M., & Knutson, B. (2005). The neuralbasis of financial risk taking. Neuron, 47, 763–770.doi:10.1016/j.neuron.2005.08.008

Laakso, M. P., Vaurio, O., Koivisto, E., Savolainen,L., Eronen, M., Aronen, H. J., . . . Tiihonen, J.(2001). Psychopathy and the posterior hippocam-pus. Behavioural Brain Research, 118, 187–193.doi:10.1016/S0166-4328(00)00324-7

Leung, M., Cheung, C., Yu, K., Yip, B., Sham, P., Li,Q., . . . McAlonan, G. (2009). Gray matter in first-episode schizophrenia before and after antipsy-chotic drug treatment. Anatomical likelihood esti-mation meta-analyses with sample size weighting.Schizophrenia Bulletin. doi: 10.1093/schbul/sbp099

Lieberman, M. D. (2000). Intuition: A social cogni-tive neuroscience approach. Psychology Bulletin,126, 109–37. doi:10.1037/0033-2909.126.1.109

Lieberman, M. D. (2007). Social cognitive neurosci-ence: A review of core processes. Annual Reviewof Psychology, 58, 259–289. doi:10.1146/annurev.psych.58.110405.085654

Lyoo, I. K., Kim, M. J., Stoll, A. L., Demopulos,C. M., Parow, A. M., Dager, S. R., . . . Renshaw,P. F. (2004). Frontal lobe gray matter density de-creases in bipolar I disorder. Biological Psychia-try, 55, 648 – 651. doi:10.1016/j.biopsych.2003.10.017

Maguire, E. A., Burgess, N., Donnett, J. G., Frack-owiak, R. S. J., Frith, C. D., & O’Keefe, J. (1998).Knowing where and getting there: A human nav-igation network science. Science, 280, 921–924.doi:10.1126/science.280.5365.921

McIlwain, D. (2003). Bypassing empathy: A Machi-avellian theory of mind and sneaky power. In B.Repacholi & V. Slaughter (Eds.), Individual dif-ferences in theory of mind: Implications for typicaland atypical development (pp. 39–66). New York:Psychology Press.

Nunnally, J. C., & Bernstein, I. H. (1994). Psycho-metric theory (3rd ed.). New York McGraw-Hill.

Ochsner, K. N., Bunge, S. A., Gross, J. J., & Gabrieli,J. D. (2002). Rethinking feelings: An FMRI studyof the cognitive regulation of emotion. Journal ofCognitive Neuroscience, 14, 1215–1229. doi:10.1162/089892902760807212

O’Doherty, J., Critchley, H., Deichmann, R., &Dolan, R. J. (2003). Dissociating valence of out-come from behavioral control in human orbital andventral prefrontal cortices. Journal of Neurosci-ence, 23, 7931–7939.

Ohira, H., Nomura, M., Ichikawa, N., Isowa, T.,Iidaka, T., Sato, A., . . . Yamada, J. (2006). Asso-ciation of neural and physiological responsesduring voluntary emotion suppression. Neuroim-

215MRI ANALYSIS OF THE MACHIAVELLIAN BRAIN

age, 29, 721–733. doi:10.1016/j.neuroimage.2005.08.047

Paulhus, D. L., & Williams, K. M. (2002). The darktriad of personality: Narcissism, Machiavellian-ism, and psychopathy. Journal of Research in Per-sonality, 36, 556 –563. doi:10.1016/S0092-6566(02)00505-6

Phillips, M. L., Drevets, W. C., Rauch, S. L., & Lane,R. (2003). Neurobiology of emotion perception I:The neural basis of normal emotion perception.Biological Psychiatry, 54, 504–514. doi:10.1016/S0006-3223(03)00168-9

Preuschoff, K., Quartz, S. R., & Bossaerts, P. (2008).Human insula activation reflects risk predictionerrors as well as risk. Journal of Neuroscience, 28,2745–2752. doi:10.1523/JNEUROSCI.4286-07.2008

Price, S., Paviour, D., Scahill, R., Stevens, J., Rossor,M., Lees, A., & Fox, N. (2004). Voxel-based mor-phometry detects patterns of atrophy that help dif-ferentiate progressive supranuclear palsy and Par-kinson’s disease. NeuroImage, 23, 663–669. doi:10.1016/j.neuroimage.2004.06.013

Repacholi, B., Slaughter, V., Pritchard, M., & Gibbs,V. (2003). Theory of mind, Machiavellianism, andsocial functioning in childhood. In B. Repacholi &V. Slaughter (Eds.), Individual differences in the-ory of mind (pp. 99–120). New York: PsychologyPress.

Rolls, E. T. (2000). The orbitofrontal cortex andreward. Cerebral Cortex, 10, 284 –294. doi:10.1093/cercor/10.3.284

Ryckman, R. M., Thornton, B., & Butler, J. (1994).Personality correlates of the hypercompetitive at-titude scale: Validity tests of Horney’s theory ofneurosis. Journal of Personality Assessment, 62,84–94. doi:10.1207/s15327752jpa6201_8

Shultz, C. J. (1993). Situational and dispositionalpredictions of performance: A test of hypothesizedMachiavellianism � structure interaction amongsales persons. Journal of Applied Social Psychol-

ogy, 23, 478–498. doi:10.1111/j.1559-1816.1993.tb01099.x

Struthers, C. W., Weiner, B., & Allred, K. (1998).Effects of causal attributions on personnel deci-sions: A social motivation perspective. Basic andApplied Social Psychology, 20, 155–166. doi:10.1207/s15324834basp2002_7

Thieben, M. J., Duggins, A. J., Good, C. D., Gomes,L., Mahant, N., Richards, F., . . . Frackowiak, R. S.(2002). The distribution of structural neuropathol-ogy in pre-clinical Huntington’s disease. Brain,125, 1815–1828. doi:10.1093/brain/awf179

Tremblay, L., & Schultz, W. (1999). Relative rewardpreference in primate orbitofrontal cortex. Nature,398, 704–708. doi:10.1038/19525

Verbeke, W. J. M. I., Ouwerkerk, C., & Peelen, E.(1996). Exploring the contextual and individualfactors on ethical decision making of salespeople.Journal of Business Ethics, 15, 1175–1187. doi:10.1007/BF00412816

Walhovd, K. B., Fjell, A. M., Reinvang, I., Luner-vold, A., Dale, A. M., Eilersten, D. E., . . . Makris,B. (2005). Effect of age on volumes of cortex,white matter and subcortical structures. Neurobi-ology of Aging, 26, 1261–1270. doi:10.1016/j.neurobiolaging.2005.05.020

White, N. S., Alkire, M. T., & Haier, R. J. (2003). Avoxel-based morphometric study of nondementedadults with Down Syndrome. NeuroImage, 20,393–403. doi:10.1016/S1053-8119(03)00273-8

Wilson, D. S., Near, D., & Miller, R. R. (1996).Machiavellianism: A synthesis of the evolutionaryand psychological literatures. Psychology Bulletin,119, 285–299.

Wrase, J., Kahnt, T., Schlagenhauf, F., Beck, A.,Cohen, M. X., Knutson, B., & Heinz, A. (2007).Different neural systems adjust motor behavior inresponse to reward and punishment. NeuroIm-age, 36, 1253–1262. doi:10.1016/j.neuroimage.2007.04.001

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